Declaration-based prompt tuning for visual question answering

• Main Authors: LIU, Yuhang, WEI, Wei, ZHU, Feida
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Machine Learning: Multi-modal learning; Computer Vision: Transfer; low-shot; semi- and un- supervised learning; Computer Vision: Vision and language; Natural Language Processing: Question Answering; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7752; https://ink.library.smu.edu.sg/context/sis_research/article/8755/viewcontent/declaration.pdf
• Institution: Singapore Management University

In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a visual-language (VL) model is first optimized via self-supervised task objectives, e.g., masked language modeling (MLM)...